Joint Resource Optimization of Shared Electric Bicycles in Smart Cities

As an important part of the green transportation of the smart city, the public electric bicycle sharing system has been deployed in more and more cities. However, users often encounter the situation that no bicycle left for rent or no available dock to return, due to many factors such as dynamic demands and stochastic usage, which seriously affects the experience of users. The state-of-art technology "borrow anywhere & return anyplace" for public bicycles is not suitable for electric bicycles since latter ones need docks for batteries recharging. Therefore, the efficient allocation of the stations, bicycles and docks, is still the key solution to the problem. This paper proposes a joint optimization of the resources allocation under the condition of limited number of stations, bicycles and docks, as well as the synthetical profiles of the candidate locations. The three factors are fully considered. Furthermore, part of resources are especially preserved for the future scheduling so that the proposed scheme can adapt to the dynamics. Users' behavior is modeled as Poisson distribution for each location. The goal of the solution is to optimize the total QoS, not that of a single station. Through the experiments on real data trace, it shows that the proposed resource allocation scheme can achieve better QoS.